A. Pinna, N. Soranzo, D. La-fuente, and A. , From knockouts to networks : establishing direct cause-effect relationships through graph analysis, PLoS One, vol.5, issue.10, p.91, 2010.

D. M. Chickering, Optimal structure identification with greedy search, The Journal of Machine Learning Research, vol.3, p.76, 2003.

P. Bühlmann, M. Kalisch, and L. Meier, High-Dimensional Statistics with a View Toward Applications in Biology, Annual Review of Statistics and Its Application, vol.1, p.58, 2014.

D. M. Chickering, D. Heckerman, and C. Meek, A Bayesian approach to learning Bayesian networks with local structure, Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, vol.43, pp.80-89, 1997.

W. Luo, K. D. Hankenson, and P. J. Woolf, Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information, BMC bioinformatics, vol.9, issue.1, 2008.

J. Watkinson, L. Kc, X. Wang, T. Zheng, and D. Anastassiou, Inference of Regulatory Gene Interactions from Expression Data Using Three-Way Mutual Information, Annals of the New York Academy of Sciences, vol.1158, issue.1, pp.302-313, 2009.

F. Emmert-streib, G. Glazko, D. Matos-simoes, and R. , Statistical inference and reverse engineering of gene regulatory networks from observational expression data, Frontiers in genetics, vol.3, 2012.

R. De-matos-simoes, M. Dehmer, and F. Emmert-streib, Interfacing cellular networks of S. cerevisiae and E. coli : Connecting dynamic and genetic information. BMC genomics, vol.14, 2013.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, p.58, 2008.

F. Fu and Q. Zhou, Learning sparse causal Gaussian networks with experimental intervention : regularization and coordinate descent, Journal of the American Statistical Association, vol.108, issue.501, p.64, 2013.

R. De-matos-simoes and F. Emmert-streib, Bagging statistical network inference from large-scale gene expression data, PLoS One, vol.7, issue.3, p.76, 2012.

K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-favera et al., Reverse engineering of regulatory networks in human B cells, Nature genetics, vol.37, issue.4, pp.382-390, 2005.

J. Pearl and . Verma, Equivalence and synthesis of causal models, Proceedings of Sixth Conference on Uncertainty in Artijicial Intelligence, vol.13, p.44, 1991.

D. Marbach, R. J. Prill, T. Schaffter, C. Mattiussi, D. Floreano et al., Revealing strengths and weaknesses of methods for gene network inference, Proceedings of the National Academy of Sciences, vol.107, issue.14, p.91, 2010.

G. Altay and F. Emmert-streib, Inferring the conservative causal core of gene regulatory networks, BMC Systems Biology, vol.4, issue.1, 2010.

M. H. Maathuis, M. Kalisch, and P. Bühlmann, Estimating high-dimensional intervention effects from observational data. The Annals of Statistics, vol.37, p.58, 2009.

A. Rau, F. Jaffrézic, and G. Nuel, Joint estimation of causal effects from observational and intervention gene expression data, BMC Systems Biology, vol.7, issue.1, p.92, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01064786

P. Spirtes, C. N. Glymour, and R. Scheines, Causation, prediction, and search, vol.81, p.76, 2000.

J. Pearl, Causality : models, reasoning and inference, vol.29, p.60, 2000.

B. Duriez, M. Sobrier, P. Duquesnoy, M. Tixier-boichard, E. Decuypere et al., A naturally occurring growth hormone receptor mutation : in vivo and in vitro evidence for the functional importance of the WS motif common to all members of the cytokine receptor superfamily, Molecular endocrinology, vol.7, issue.6, p.52, 1993.

G. K. Smyth, Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments, In : Statistical Applications in Genetics and Molecular Biology, vol.9, p.52, 2004.

A. Hauser and P. Bühlmann, Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs, vol.13, p.92, 2012.

F. Emmert-streib, M. Dehmer, and B. Haibe-kains, Gene regulatory networks and their applications : understanding biological and medical problems in terms of networks, Frontiers in Cell and Developmental Biology, vol.2, p.50, 2014.

P. D. Hsu, E. S. Lander, and F. Zhang, Development and applications of CRISPR-Cas9 for genome engineering, Cell, vol.157, issue.6, pp.1262-1278, 2014.

, Limites de l'inférence causale

, Une double contrainte s'applique. D'une part, on est poussé à inclure le minimum de variables possible pour lutter contre la dimension du problème. D'autre part, on est aussi tenté d'en inclure un maximum pour limiter le risque qu'induisent des variables cachées. La capacité d'obtenir ces données étant aussi une limite dans ce dilemme. Cela explique l'émergence des réseaux de régulation génique. Ceux-ci n'ont pas de réalité physique directe, et ne modélisent donc pas une cascade d'interactions classique comme peuvent avoir l'habitude les biologistes. Cependant, le coût pour obtenir des données d'expressions de gènes est faible, contrairement à d'autres types de données, comme les métabolites. C'est pourquoi il est plus facile de travailler sur des modèles n'incluant que celles-ci, Si l'inférence causale est aujourd'hui en partie possible, c'est grâce à notre capacité à capter et traiter un grand nombre de données. En effet, les réseaux orientés sont d'une complexité super-exponentielle, si bien qu'un réseau de 5 noeuds n'est déjà plus traitable

, La méthodologie actuelle a aussi ses limites. L'analyse différentielle, à la suite d'une intervention, permet de saisir un ensemble de gènes candidats pour établir une chaîne de causalité. Cependant, elle est limitée par la possibilité de l'effectuer, certains gènes éteints pouvant mener directement à la mort ou au mauvais développement de l'organisme. De plus, elle ne permet que d'analyser la chaîne causale d

. L'inférence-de-réseaux and &. Qu, elle soit faite par tests ou par scores, est limitée par la complexité du problème. La méthode la plus fiable semble être l'approche gloutonne, qui n'offre aucune garantie globale de convergence. De plus, certaines structures sont particulièrement difficiles à estimer

R. Albert, Scale-free networks in cell biology, Journal of cell science, vol.118, issue.21, pp.4947-4957, 2005.

G. Altay and F. Emmert-streib, Inferring the conservative causal core of gene regulatory networks, BMC Systems Biology, vol.4, issue.1, 2010.

S. Anders and W. Huber, Differential expression analysis for sequence count data, Genome biology, vol.11, issue.10, 2010.

S. A. Andersson, D. Madigan, and M. D. Perlman, A characterization of markov equivalence classes for acyclic digraphs, The Annals of Statistics, vol.25, issue.2, pp.505-541, 1997.

S. Banerjee and S. Ghosal, Bayesian structure learning in graphical models, Journal of Multivariate Analysis, vol.136, pp.147-162, 2015.

D. Barker, S. Hill, and S. Mukherjee, Mc 4 : a tempering algorithm for largesample network inference, Pattern Recognition in Bioinformatics, vol.96, pp.431-442, 2010.

K. Basso, A. A. Margolin, G. Stolovitzky, U. Klein, R. Dalla-favera et al., Reverse engineering of regulatory networks in human b cells, Nature genetics, vol.37, issue.4, pp.382-390, 2005.

E. Ben-david, T. Li, H. Massam, and B. Rajaratnam, High dimensional bayesian inference for gaussian directed acyclic graph models, 2011.

P. Bühlmann, M. Kalisch, and L. Meier, High-dimensional statistics with a view toward applications in biology, Annual Review of Statistics and Its Application, vol.1, p.58, 2014.

R. Bumgarner, Overview of dna microarrays : types, applications, and their future. Current protocols in molecular biology, pp.22-23, 2013.

A. J. Butte and I. S. Kohane, Mutual information relevance networks : functional genomic clustering using pairwise entropy measurements, Pac Symp Biocomput, vol.5, p.26, 2000.

V. ,

L. M. Campos, A scoring function for learning bayesian networks based on mutual information and conditional independence tests, Journal of Machine Learning Research, vol.7, issue.10, pp.2149-2187, 2006.

S. L. Carter, C. M. Brechbühler, M. Griffin, and A. T. Bond, Gene coexpression network topology provides a framework for molecular characterization of cellular state, Bioinformatics, vol.20, issue.14, pp.2242-2250, 2004.

N. Cartwright and E. Mcmullin, How the laws of physics lie, vol.14, 1984.

D. M. Chickering, Optimal structure identification with greedy search, The Journal of Machine Learning Research, vol.3, p.76, 2003.

D. M. Chickering and D. Heckerman, Efficient approximations for the marginal likelihood of bayesian networks with hidden variables, Machine learning, vol.29, issue.2-3, pp.181-212, 1997.

D. M. Chickering, D. Heckerman, and C. Meek, A bayesian approach to learning bayesian networks with local structure, Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence, p.43, 1997.

B. Clarke, B. Leuridan, and J. Williamson, Modelling mechanisms with causal cycles, Synthese, vol.191, issue.8, pp.1651-1681, 2014.

A. De-la-fuente, N. Bing, I. Hoeschele, and P. Mendes, Discovery of meaningful associations in genomic data using partial correlation coefficients, Bioinformatics, vol.20, issue.18, pp.3565-3574, 2004.

R. De-matos-simoes, M. Dehmer, and F. Emmert-streib, Interfacing cellular networks of s. cerevisiae and e. coli : Connecting dynamic and genetic information, BMC genomics, vol.14, issue.1, 2013.

R. De-matos-simoes and F. Emmert-streib, Bagging statistical network inference from large-scale gene expression data, PLoS One, vol.7, issue.3, p.76, 2012.

A. Dobra, T. S. Eicher, and A. Lenkoski, Modeling uncertainty in macroeconomic growth determinants using gaussian graphical models, Statistical Methodology, vol.7, issue.3, pp.292-306, 2010.

J. Doignon, A. Peke?, and M. Regenwetter, The repeated insertion model for rankings : Missing link between two subset choice models, Psychometrika, vol.69, issue.1, pp.33-54, 2004.

D. C. Dolinoy, The agouti mouse model : an epigenetic biosensor for nutritional and environmental alterations on the fetal epigenome, Nutrition reviews, vol.66, issue.suppl_1, pp.7-11, 2008.

B. Duriez, M. Sobrier, P. Duquesnoy, M. Tixier-boichard, E. Decuypere et al., A naturally occurring growth hormone receptor mutation : in vivo and in vitro evidence for the functional importance of the ws motif common to all members of the cytokine receptor superfamily, Molecular endocrinology, vol.7, issue.6, p.52, 1993.

P. D'haeseleer, S. Liang, and R. Somogyi, Genetic network inference : from co-expression clustering to reverse engineering, Bioinformatics, vol.16, issue.8, pp.707-726, 2000.

F. Emmert-streib, G. Glazko, and R. De-matos-simoes, Statistical inference and reverse engineering of gene regulatory networks from observational expression data, Frontiers in genetics, 2012.

E. O. Folmer, M. Van-der-geest, E. Jansen, H. Olff, T. M. Anderson et al., Seagrass-sediment feedback : an exploration using a nonrecursive structural equation model, Ecosystems, vol.15, issue.8, pp.1380-1393, 2012.

J. Friedman, T. Hastie, and R. Tibshirani, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, vol.9, issue.3, p.58, 2008.

N. Friedman and D. Koller, Being bayesian about network structure. a bayesian approach to structure discovery in bayesian networks, Machine learning, vol.50, issue.1-2, p.86, 2003.

N. Friedman, M. Linial, I. Nachman, and D. Pe'er, Using bayesian networks to analyze expression data, Journal of computational biology, vol.7, issue.3-4, 2000.

F. Fu and Q. Zhou, Learning sparse causal gaussian networks with experimental intervention : regularization and coordinate descent, Journal of the American Statistical Association, vol.108, issue.501, p.64, 2013.

D. Geiger and D. Heckerman, Parameter priors for directed acyclic graphical models and the characterization of several probability distributions, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, p.86, 1999.

A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari et al., Bayesian data analysis, vol.2, 2014.

S. B. Gillispie and M. D. Perlman, Enumerating markov equivalence classes of acyclic digraph dels, Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence, pp.171-177, 2001.

R. J. Goudie and S. Mukherjee, A gibbs sampler for learning dags, The Journal of Machine Learning Research, vol.17, issue.1, pp.1032-1070, 2016.

C. W. Granger, Investigating causal relations by econometric models and cross-spectral methods, Econometrica : Journal of the Econometric Society, vol.15, pp.424-438, 1969.

A. Greenfield, C. Hafemeister, and R. Bonneau, Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks, Bioinformatics, vol.29, issue.8, pp.1060-1067, 2013.

M. Grzegorczyk and D. Husmeier, Improving the structure mcmc sampler for bayesian networks by introducing a new edge reversal move, Machine Learning, vol.71, pp.265-305, 2008.

K. D. Hansen, R. A. Irizarry, and Z. Wu, Removing technical variability in rna-seq data using conditional quantile normalization, Biostatistics, vol.13, issue.2, pp.204-216, 2012.

W. K. Hastings, Monte carlo sampling methods using markov chains and their applications, Biometrika, vol.57, issue.1, p.78, 1970.

A. Hauser and P. Bühlmann, Characterization and greedy learning of interventional markov equivalence classes of directed acyclic graphs, Journal of Machine Learning Research, vol.13, p.92, 2012.

A. Hauser and P. Bühlmann, Jointly interventional and observational data : estimation of interventional markov equivalence classes of directed acyclic graphs, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.77, issue.1, pp.291-318, 2015.

Y. He, J. Jia, and B. Yu, Reversible mcmc on markov equivalence classes of sparse directed acyclic graphs, The Annals of Statistics, vol.41, issue.4, pp.1742-1779, 2013.

J. M. Heather and B. Chain, The sequence of sequencers : The history of sequencing dna, Genomics, vol.107, issue.1, pp.1-8, 2016.

P. D. Hsu, E. S. Lander, and F. Zhang, Development and applications of crispr-cas9 for genome engineering, Cell, vol.157, issue.6, pp.1262-1278, 2014.

A. Hyttinen, F. Eberhardt, and P. O. Hoyer, Learning linear cyclic causal models with latent variables, Journal of Machine Learning Research, vol.13, pp.3387-3439, 2012.

M. Kalisch and P. Bühlmann, Estimating high-dimensional directed acyclic graphs with the pc-algorithm, The Journal of Machine Learning Research, vol.8, p.58, 2007.

J. Kasza and P. Solomon, Comparing score-based methods for estimating bayesian networks using the kullback-leibler divergence, Communications in Statistics-Theory and Methods, vol.44, issue.1, pp.135-152, 2015.

S. Y. Kim, S. Imoto, and S. Miyano, Inferring gene networks from time series microarray data using dynamic bayesian networks, Briefings in bioinformatics, vol.4, issue.3, pp.228-235, 2003.

M. Kistler, La causalité dans la philosophie contemporaine. Intellectica, vol.38, pp.139-185, 2004.

D. Koller and N. Friedman, Probabilistic graphical models : principles and techniques, p.10, 2009.

J. Krumsiek, K. Suhre, T. Illig, J. Adamski, and F. J. Theis, Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data, BMC systems biology, vol.5, issue.1, 2011.

G. Lacerda, P. L. Spirtes, J. Ramsey, and P. O. Hoyer, Discovering cyclic causal models by independent components analysis, 2012.

S. L. Lauritzen, Graphical models, Clarendon Press, vol.17, p.30, 1996.

C. W. Law, Y. Chen, W. Shi, and G. K. Smyth, Voom : precision weights unlock linear model analysis tools for rna-seq read counts, Genome biology, vol.15, issue.2, 2014.

W. Lee and W. Tzou, Computational methods for discovering gene networks from expression data, Briefings in bioinformatics, vol.10, issue.4, pp.408-423, 2009.

A. Lesne, Biologie des systèmes-l'organisation multiéchelle des systèmes vivants. médecine/sciences, vol.25, pp.585-587, 2009.

M. H. Maathuis, D. Colombo, M. Kalisch, and P. Bühlmann, Predicting causal effects in large-scale systems from observational data, Nature Methods, vol.7, issue.4, p.76, 2010.

M. H. Maathuis, M. Kalisch, and P. Bühlmann, Estimating highdimensional intervention effects from observational data, The Annals of Statistics, vol.37, issue.6A, p.58, 2009.

D. Madigan, J. York, and D. Allard, Bayesian graphical models for discrete data, International Statistical Review/Revue Internationale de Statistique, vol.24, p.91, 1995.

D. Madigan and J. C. York, Bayesian methods for estimation of the size of a closed population, Biometrika, vol.84, issue.1, pp.19-31, 1997.

C. L. Mallows, Non-null ranking models. i. Biometrika, vol.62, p.78, 1957.

J. Marin and C. Robert, Bayesian core : a practical approach to computational Bayesian statistics, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00450489

N. Meinshausen, A. Hauser, J. M. Mooij, J. Peters, P. Versteeg et al., Methods for causal inference from gene perturbation experiments and validation, Proceedings of the National Academy of Sciences, vol.113, pp.7361-7368, 2016.

J. A. Miller, C. Cai, P. Langfelder, D. H. Geschwind, S. M. Kurian et al., Strategies for aggregating gene expression data : the collapserows r function, BMC bioinformatics, vol.12, issue.1, pp.322-331, 2011.

J. Mooij and T. Heskes, Cyclic causal discovery from continuous equilibrium data, 2013.

G. Nuel, A. Rau, J. , and F. , Joint likelihood calculation for intervention and observational data from a gaussian bayesian network, vol.76, p.77, 2013.

R. Nunkoo, H. Ramkissoon, and D. Gursoy, Use of structural equation modeling in tourism research : past, present, and future, Journal of Travel Research, vol.52, issue.6, pp.759-771, 2013.

J. Pearl, Bayesian networks : A model of self-activated memory for evidential reasoning, 1985.

J. Pearl, Causality : models, reasoning and inference, vol.29, p.60, 2000.

A. Pinna, S. Heise, R. J. Flassig, A. De-la-fuente, and S. Klamt, Reconstruction of large-scale regulatory networks based on perturbation graphs and transitive reduction : improved methods and their evaluation, BMC Systems Biology, vol.7, issue.1, p.58, 2013.

A. Pinna, N. Soranzo, and A. De-la-fuente, From knockouts to networks : establishing direct cause-effect relationships through graph analysis, PLoS One, vol.5, issue.10, p.91, 2010.

T. Richardson, Equivalence in non-recursive structural equation models, Compstat, vol.30, p.38, 1994.

T. Richardson, A polynomial algorithm for deciding equivalence in directed cyclic graphical models, vol.32, p.38, 1995.

T. Richardson, A discovery algorithm for directed cyclic graphs, Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence, p.37, 1996.

G. O. Roberts, A. Gelman, and W. R. Gilks, Weak convergence and optimal scaling of random walk metropolis algorithms. The annals of applied probability, vol.7, pp.110-120, 1997.

R. W. Robinson, Counting unlabeled acyclic digraphs. Combinatorial mathematics V, vol.622, pp.28-43, 1977.

E. Rocha, Analyse exploratoire des génomes bactériens. Génétique cellulaire et moléculaire, 2000.

A. Roverato and G. Consonni, Compatible prior distributions for directed acyclic graph models, Journal of the Royal Statistical Society : Series B (Statistical Methodology), vol.66, issue.1, pp.47-61, 2004.

D. B. Rubin, Estimating causal effects of treatments in randomized and nonrandomized studies, Journal of educational Psychology, vol.66, issue.5, 1974.

B. Russell, On the notion of cause, Proceedings of the Aristotelian society, vol.13, p.14, 1912.

F. Sanger, S. Nicklen, and A. R. Coulson, Dna sequencing with chainterminating inhibitors, Proceedings of the national academy of sciences, vol.74, pp.5463-5467, 1977.

F. Sanger and E. Thompson, The amino-acid sequence in the glycyl chain of insulin. 1. the identification of lower peptides from partial hydrolysates, Biochemical Journal, vol.53, issue.3, pp.353-359, 1953.

H. Schawe, R. Bleim, and A. K. Hartmann, Phase transitions of the typical algorithmic complexity of the random satisfiability problem studied with linear programming, 2017.

M. Schmidt and K. Murphy, Modeling discrete interventional data using directed cyclic graphical models, Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, p.30, 2009.

G. Schwarz, Estimating the dimension of a model. The annals of statistics, vol.6, pp.461-464, 1978.

G. L. Sen and H. M. Blau, A brief history of rnai : the silence of the genes, The FASEB journal, vol.20, issue.9, pp.1293-1299, 2006.

O. Shalem, N. E. Sanjana, E. Hartenian, X. Shi, D. A. Scott et al., Genome-scale crispr-cas9 knockout screening in human cells, Science, vol.343, issue.6166, pp.84-87, 2014.

G. K. Smyth, Linear models and empirical bayes methods for assessing differential expression in microarray experiments, In Statistical Applications in Genetics and Molecular Biology, vol.9, p.52, 2004.

E. V. Someren, L. Wessels, E. Backer, and M. Reinders, Genetic network modeling, Pharmacogenomics, vol.3, issue.4, pp.507-525, 2002.

P. Spirtes, Conditional independence in directed cyclic graphical models for feedback, p.30, 1994.

P. Spirtes, Directed cyclic graphical representations of feedback models, Proceedings of the Eleventh conference on Uncertainty in artificial intelligence, pp.491-498, 1995.

P. Spirtes, C. Glymour, and R. Scheines, From probability to causality. Philosophical Studies, vol.64, pp.1-36, 1991.

P. Spirtes, C. N. Glymour, and R. Scheines, Causation, prediction, and search, vol.81, p.76, 2000.

R. H. Strotz and H. O. Wold, Recursive vs. nonrecursive systems : An attempt at synthesis (part i of a triptych on causal chain systems), Econometrica : Journal of the Econometric Society, vol.30, pp.417-427, 1960.

I. Tsamardinos, L. E. Brown, A. , and C. F. , The max-min hill-climbing bayesian network structure learning algorithm, Machine learning, vol.65, issue.1, p.76, 2006.

T. V. Udea-pearl, Equivalence and synthesis of causal models, Proceedings of Sixth Conference on Uncertainty in Artijicial Intelligence, vol.13, p.44, 1991.

J. Watkinson, K. Liang, X. Wang, T. Zheng, and D. Anastassiou, Inference of regulatory gene interactions from expression data using three-way mutual information, vol.1158, pp.302-313, 2009.

J. Watson and F. Crick, Molecular structure of nucleic acids : A structure for deoxyribose nucleic acid, Nature, vol.171, issue.6, pp.3-12, 1953.

M. T. Weirauch, Gene coexpression networks for the analysis of dna microarray data. Applied statistics for network biology : methods in systems biology, pp.215-250, 2011.

Y. Weiss and W. T. Freeman, Correctness of belief propagation in gaussian graphical models of arbitrary topology, Advances in neural information processing systems, vol.17, pp.673-679, 2000.

S. Wright, Correlation and causation, Journal of agricultural research, vol.20, issue.7, pp.557-585, 1921.

S. Wright, The method of path coefficients. The annals of mathematical statistics, vol.5, pp.161-215, 1934.

B. Zhang and S. Horvath, A general framework for weighted gene coexpression network analysis, Statistical applications in genetics and molecular biology, vol.4, 2005.